{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "84d7c68d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler, OneHotEncoder   # 预处理\n",
    "from sklearn.cluster import KMeans   # 聚类算法\n",
    "from sklearn.metrics import silhouette_score   # 用于评估度量的模块\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "94b36e22",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']   # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False    # 用来正常显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "12d705ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>渠道代号</th>\n",
       "      <th>日均UV</th>\n",
       "      <th>平均注册率</th>\n",
       "      <th>平均搜索量</th>\n",
       "      <th>访问深度</th>\n",
       "      <th>平均停留时间</th>\n",
       "      <th>订单转化率</th>\n",
       "      <th>投放总时间</th>\n",
       "      <th>素材类型</th>\n",
       "      <th>广告类型</th>\n",
       "      <th>合作方式</th>\n",
       "      <th>广告尺寸</th>\n",
       "      <th>广告卖点</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A203</td>\n",
       "      <td>3.69</td>\n",
       "      <td>0.0071</td>\n",
       "      <td>0.0214</td>\n",
       "      <td>2.3071</td>\n",
       "      <td>419.77</td>\n",
       "      <td>0.0258</td>\n",
       "      <td>20</td>\n",
       "      <td>jpg</td>\n",
       "      <td>banner</td>\n",
       "      <td>roi</td>\n",
       "      <td>140*40</td>\n",
       "      <td>打折</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A387</td>\n",
       "      <td>178.70</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0324</td>\n",
       "      <td>2.0489</td>\n",
       "      <td>157.94</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>19</td>\n",
       "      <td>jpg</td>\n",
       "      <td>banner</td>\n",
       "      <td>cpc</td>\n",
       "      <td>140*40</td>\n",
       "      <td>满减</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   渠道代号    日均UV   平均注册率   平均搜索量    访问深度  平均停留时间   订单转化率  投放总时间 素材类型    广告类型  \\\n",
       "0  A203    3.69  0.0071  0.0214  2.3071  419.77  0.0258     20  jpg  banner   \n",
       "1  A387  178.70  0.0040  0.0324  2.0489  157.94  0.0030     19  jpg  banner   \n",
       "\n",
       "  合作方式    广告尺寸 广告卖点  \n",
       "0  roi  140*40   打折  \n",
       "1  cpc  140*40   满减  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取数据\n",
    "data = pd.read_csv('ad_performance.csv', index_col=0)\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da809fa",
   "metadata": {},
   "source": [
    "### 数据清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "21133466",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>日均UV</th>\n",
       "      <td>889.0</td>\n",
       "      <td>540.8468</td>\n",
       "      <td>1634.4105</td>\n",
       "      <td>0.06</td>\n",
       "      <td>6.1800</td>\n",
       "      <td>114.1800</td>\n",
       "      <td>466.8700</td>\n",
       "      <td>25294.7700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>平均注册率</th>\n",
       "      <td>889.0</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0014</td>\n",
       "      <td>0.0391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>平均搜索量</th>\n",
       "      <td>889.0</td>\n",
       "      <td>0.0305</td>\n",
       "      <td>0.1062</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.0118</td>\n",
       "      <td>1.0370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>访问深度</th>\n",
       "      <td>889.0</td>\n",
       "      <td>2.1672</td>\n",
       "      <td>3.8005</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.3923</td>\n",
       "      <td>1.7931</td>\n",
       "      <td>2.2162</td>\n",
       "      <td>98.9799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>平均停留时间</th>\n",
       "      <td>887.0</td>\n",
       "      <td>262.6692</td>\n",
       "      <td>224.3649</td>\n",
       "      <td>1.64</td>\n",
       "      <td>126.0200</td>\n",
       "      <td>236.5500</td>\n",
       "      <td>357.9850</td>\n",
       "      <td>4450.8300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>订单转化率</th>\n",
       "      <td>889.0</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>0.2165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>投放总时间</th>\n",
       "      <td>889.0</td>\n",
       "      <td>16.0529</td>\n",
       "      <td>8.5094</td>\n",
       "      <td>1.00</td>\n",
       "      <td>9.0000</td>\n",
       "      <td>16.0000</td>\n",
       "      <td>24.0000</td>\n",
       "      <td>30.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count      mean        std   min       25%       50%       75%  \\\n",
       "日均UV    889.0  540.8468  1634.4105  0.06    6.1800  114.1800  466.8700   \n",
       "平均注册率   889.0    0.0014     0.0033  0.00    0.0000    0.0000    0.0014   \n",
       "平均搜索量   889.0    0.0305     0.1062  0.00    0.0006    0.0032    0.0118   \n",
       "访问深度    889.0    2.1672     3.8005  1.00    1.3923    1.7931    2.2162   \n",
       "平均停留时间  887.0  262.6692   224.3649  1.64  126.0200  236.5500  357.9850   \n",
       "订单转化率   889.0    0.0029     0.0116  0.00    0.0000    0.0002    0.0020   \n",
       "投放总时间   889.0   16.0529     8.5094  1.00    9.0000   16.0000   24.0000   \n",
       "\n",
       "               max  \n",
       "日均UV    25294.7700  \n",
       "平均注册率       0.0391  \n",
       "平均搜索量       1.0370  \n",
       "访问深度       98.9799  \n",
       "平均停留时间   4450.8300  \n",
       "订单转化率       0.2165  \n",
       "投放总时间      30.0000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对数据进行审查：是否有缺失值（根据描述性统计可见“平均停留时间有两个NAN值”）\n",
    "data.describe().round(4).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "79e7a7dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*************************填充缺失值：填充前**************************\n",
      "     渠道代号  日均UV  平均注册率  平均搜索量  访问深度  平均停留时间  订单转化率  投放总时间 素材类型 广告类型 合作方式  \\\n",
      "323  A347  1.53    0.0    0.0   1.0     NaN    0.0     26  swf  不确定  roi   \n",
      "354  A377  0.75    0.0    0.0   1.0     NaN    0.0     20  swf  不确定  roi   \n",
      "\n",
      "       广告尺寸 广告卖点  \n",
      "323  600*90   打折  \n",
      "354  600*90   打折  \n",
      "*************************填充缺失值：填充后**************************\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>渠道代号</th>\n",
       "      <th>日均UV</th>\n",
       "      <th>平均注册率</th>\n",
       "      <th>平均搜索量</th>\n",
       "      <th>访问深度</th>\n",
       "      <th>平均停留时间</th>\n",
       "      <th>订单转化率</th>\n",
       "      <th>投放总时间</th>\n",
       "      <th>素材类型</th>\n",
       "      <th>广告类型</th>\n",
       "      <th>合作方式</th>\n",
       "      <th>广告尺寸</th>\n",
       "      <th>广告卖点</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [渠道代号, 日均UV, 平均注册率, 平均搜索量, 访问深度, 平均停留时间, 订单转化率, 投放总时间, 素材类型, 广告类型, 合作方式, 广告尺寸, 广告卖点]\n",
       "Index: []"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对缺失值的填充（均值）\n",
    "print('{:*^60}'.format('填充缺失值：填充前'))\n",
    "print(data[data.isna().values])\n",
    "data = data.fillna(data['平均停留时间'].mean())\n",
    "print('{:*^60}'.format('填充缺失值：填充后'))\n",
    "data[data.isna().values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a2379ce8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['渠道代号', '日均UV', '平均注册率', '平均搜索量', '访问深度', '订单转化率', '投放总时间', '素材类型',\n",
      "       '广告类型', '合作方式', '广告尺寸', '广告卖点'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# 计算、合并：相关性\n",
    "data.corr().round(4).T\n",
    "# 生成相关性统计表格\n",
    "data.corr().round(4).T.to_excel('corr.xlsx')\n",
    "data = data.drop(['平均停留时间'], axis=1)\n",
    "print(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5e53fece",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        日均UV   平均注册率   平均搜索量     访问深度   订单转化率  投放总时间\n",
      "0       3.69  0.0071  0.0214   2.3071  0.0258     20\n",
      "1     178.70  0.0040  0.0324   2.0489  0.0030     19\n",
      "2      91.77  0.0022  0.0530   1.8771  0.0026      4\n",
      "3       1.09  0.0074  0.3382   4.2426  0.0153     10\n",
      "4       3.37  0.0028  0.1740   2.1934  0.0007     30\n",
      "..       ...     ...     ...      ...     ...    ...\n",
      "884  1777.75  0.0002  0.0023   1.2588  0.0002      5\n",
      "885  1953.53  0.0003  0.0026   1.1703  0.0002     16\n",
      "886   310.53  0.0002  0.0028   1.1546  0.0001     22\n",
      "887  1370.38  0.0001  0.0016   1.3939  0.0001     10\n",
      "888     0.86  0.0000  0.0000  52.6591  0.0000     19\n",
      "\n",
      "[889 rows x 6 columns]\n",
      "**********************数据标准化：归一化Min-Max**********************\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.  , 0.18, 0.02, 0.01, 0.12, 0.66],\n",
       "       [0.01, 0.1 , 0.03, 0.01, 0.01, 0.62],\n",
       "       [0.  , 0.06, 0.05, 0.01, 0.01, 0.1 ],\n",
       "       ...,\n",
       "       [0.01, 0.01, 0.  , 0.  , 0.  , 0.72],\n",
       "       [0.05, 0.  , 0.  , 0.  , 0.  , 0.31],\n",
       "       [0.  , 0.  , 0.  , 0.53, 0.  , 0.62]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据标准化：归一化Min-Max，0-1区间（针对数值）\n",
    "print(data.iloc[:, 1:7])\n",
    "matrix = data.iloc[:, 1:7]\n",
    "min_max_model = MinMaxScaler()\n",
    "data_rescaled = min_max_model.fit_transform(matrix)\n",
    "print('{:*^60}'.format('数据标准化：归一化Min-Max'))\n",
    "data_rescaled.round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bc539311",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  素材类型    广告类型 合作方式    广告尺寸 广告卖点\n",
      "0  jpg  banner  roi  140*40   打折\n",
      "1  jpg  banner  cpc  140*40   满减\n",
      "*************************特征数字化：独热编码*************************\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0., 1., 0., ..., 0., 0., 0.],\n",
       "       [0., 1., 0., ..., 0., 0., 0.],\n",
       "       [0., 1., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [1., 0., 0., ..., 0., 1., 0.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征数字化：独热编码（One-Hot）（针对非数值）\n",
    "print(data.iloc[:, 7:12].head(2))\n",
    "onehot_mode = OneHotEncoder(sparse=False)\n",
    "data_ohe = onehot_mode.fit_transform(data.iloc[:, 7:12])\n",
    "print('{:*^60}'.format('特征数字化：独热编码'))\n",
    "data_ohe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2189eea1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.  , 0.18, 0.02, ..., 0.  , 0.  , 0.  ],\n",
       "       [0.01, 0.1 , 0.03, ..., 0.  , 0.  , 0.  ],\n",
       "       [0.  , 0.06, 0.05, ..., 0.  , 0.  , 0.  ],\n",
       "       ...,\n",
       "       [0.01, 0.01, 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "       [0.05, 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "       [0.  , 0.  , 0.  , ..., 0.  , 1.  , 0.  ]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 数据合并\n",
    "data_matrix = np.hstack((data_rescaled, data_ohe))\n",
    "data_matrix.round(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f3dbb20",
   "metadata": {},
   "source": [
    "### 聚类分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e4a95af2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "**********************所有的K值以及对应的平均轮廓系数**********************\n",
      "[[2, 0.3865549293769709], [3, 0.45757882622575624], [4, 0.5020981194788054], [5, 0.48003589664576785]]\n",
      "最佳K值： 4\n",
      "***************************聚类分析结果***************************\n"
     ]
    },
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>counts</th>\n",
       "      <td>154</td>\n",
       "      <td>349</td>\n",
       "      <td>313</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percentage</th>\n",
       "      <td>0.173</td>\n",
       "      <td>0.393</td>\n",
       "      <td>0.352</td>\n",
       "      <td>0.082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>日均UV</th>\n",
       "      <td>613.836</td>\n",
       "      <td>300.205</td>\n",
       "      <td>572.521</td>\n",
       "      <td>1401.525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>平均注册率</th>\n",
       "      <td>0.003</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>平均搜索量</th>\n",
       "      <td>0.02</td>\n",
       "      <td>0.016</td>\n",
       "      <td>0.051</td>\n",
       "      <td>0.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>访问深度</th>\n",
       "      <td>2.19</td>\n",
       "      <td>2.27</td>\n",
       "      <td>2.145</td>\n",
       "      <td>1.727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>订单转化率</th>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>投放总时间</th>\n",
       "      <td>15.682</td>\n",
       "      <td>15.35</td>\n",
       "      <td>17.125</td>\n",
       "      <td>15.603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>素材类型</th>\n",
       "      <td>jpg</td>\n",
       "      <td>jpg</td>\n",
       "      <td>swf</td>\n",
       "      <td>swf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广告类型</th>\n",
       "      <td>banner</td>\n",
       "      <td>横幅</td>\n",
       "      <td>不确定</td>\n",
       "      <td>tips</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>合作方式</th>\n",
       "      <td>cpc</td>\n",
       "      <td>cpc</td>\n",
       "      <td>roi</td>\n",
       "      <td>cpm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广告尺寸</th>\n",
       "      <td>308*388</td>\n",
       "      <td>600*90</td>\n",
       "      <td>600*90</td>\n",
       "      <td>450*300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广告卖点</th>\n",
       "      <td>满减</td>\n",
       "      <td>直降</td>\n",
       "      <td>打折</td>\n",
       "      <td>打折</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0        1        2         3\n",
       "counts          154      349      313        73\n",
       "percentage    0.173    0.393    0.352     0.082\n",
       "日均UV        613.836  300.205  572.521  1401.525\n",
       "平均注册率         0.003    0.001    0.001     0.001\n",
       "平均搜索量          0.02    0.016    0.051     0.033\n",
       "访问深度           2.19     2.27    2.145     1.727\n",
       "订单转化率         0.003    0.002    0.004     0.002\n",
       "投放总时间        15.682    15.35   17.125    15.603\n",
       "素材类型            jpg      jpg      swf       swf\n",
       "广告类型         banner       横幅      不确定      tips\n",
       "合作方式            cpc      cpc      roi       cpm\n",
       "广告尺寸        308*388   600*90   600*90   450*300\n",
       "广告卖点             满减       直降       打折        打折"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# KMeans建模：基于平均轮廓系数，找到最佳值（2-10）\n",
    "# 关于K的取值范围：2-5\n",
    "score_list = []\n",
    "max_score = -1  # score取值范围（-1，1）\n",
    "\n",
    "for k in range(2, 6):\n",
    "    kmeans_model = KMeans(n_clusters=k, random_state=1)  # 建模\n",
    "    kmeans_temp = kmeans_model.fit_predict(data_matrix)  # 计算点距离\n",
    "    score = silhouette_score(data_matrix, kmeans_temp)  # 得到每个K下的平均轮廓系数\n",
    "    score_list.append([k, score])\n",
    "    # 获取最佳K值\n",
    "    if score > max_score:\n",
    "        max_score = score  # 保存更高的系数值\n",
    "        best_k = k  # 保存最佳的K值\n",
    "        labels_temp = kmeans_temp  # 保存数据标签\n",
    "\n",
    "print('{:*^60}'.format('所有的K值以及对应的平均轮廓系数'))\n",
    "print(score_list)\n",
    "print('最佳K值：', best_k)\n",
    "\n",
    "# 聚类结果分析\n",
    "# 1、合并数据与聚类标签\n",
    "cluster_labels = pd.DataFrame(labels_temp, columns=['clusters'])  # 将聚类标签转化为df\n",
    "merge_data = pd.concat((data, cluster_labels), axis=1)  # 整合原始数据与聚类标签\n",
    "\n",
    "# 2、各聚类下的样本量\n",
    "cluster_counts = pd.DataFrame(merge_data['渠道代号'].groupby(merge_data['clusters']).count()).T.rename({'渠道代号':'counts'})\n",
    "\n",
    "# 3、各聚类下的样本占比\n",
    "cluster_percents = (cluster_counts / len(data)).round(3).rename({'counts': 'percentage'})\n",
    "features = []\n",
    "for label in range(best_k):\n",
    "    label_data = merge_data[merge_data['clusters'] == label]\n",
    "    \n",
    "    # 4、数值类特征的均值\n",
    "    p1_data = label_data.iloc[:, 1:7]  # 筛选出数值类特征\n",
    "    p1_des = p1_data.describe().round(3) # 获取描述性统计信息\n",
    "    p1_mean = p1_des.iloc[1, :]  # 获取均值数据\n",
    "    \n",
    "    # 5、字符类特征的众数\n",
    "    p2_data = label_data.iloc[:, 7:12]  # 筛选出字符类特征\n",
    "    p2_des = p2_data.describe() \n",
    "    p2_mode = p2_des.iloc[2, :]  # 获取频数最高的标签\n",
    "    \n",
    "    # 横向拼接2类不同特征的数据\n",
    "    merge_line = pd.concat((p1_mean, p2_mode), axis=0)\n",
    "    # 纵向拼接4类簇的统计数据\n",
    "    features.append(merge_line)\n",
    "    \n",
    "# 6、数据合并和展示\n",
    "cluster_pd = pd.DataFrame(features).T\n",
    "print('{:*^60}'.format('聚类分析结果'))\n",
    "all_cluster_pd = pd.concat((cluster_counts, cluster_percents, cluster_pd), axis=0)\n",
    "pd.DataFrame(all_cluster_pd).to_excel('聚类结果分析.xlsx')\n",
    "all_cluster_pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c80f218",
   "metadata": {},
   "source": [
    " 结论：\n",
    " - 渠道分类4个类别，样本数据分别为154，349，313，73，其中第四类样本量较少。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87b3f037",
   "metadata": {},
   "source": [
    "### 可视化：雷达图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "98694d9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1、获取各簇的数值特征值均值，并且标准化（Max-Min归一化，0~1）\n",
    "nums_data = cluster_pd.iloc[:6,:].T.astype(np.float64)\n",
    "nums_min_max = min_max_model.fit_transform(nums_data)\n",
    "\n",
    "# 2、绘制画布、准备数据：x轴角度、y轴数据、类别对应颜色\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "ax = fig.add_subplot(111, polar=True)  # 创建子网格：正中央、极坐标系\n",
    "angles = np.linspace(0, 2 * np.pi, 6, endpoint=False) # 计算角度\n",
    "angles = np.concatenate((angles, [angles[0]]))  # 完成了对于x轴的设置，并且最后一个值=第一个值，以闭合图形\n",
    "colors = ['b', 'y', 'r', 'g']\n",
    "labels = p1_data.columns.tolist()\n",
    "labels = np.concatenate((labels,[labels[0]]))\n",
    "\n",
    "# 3、绘制各簇对应的点线图\n",
    "for i in range(len(nums_min_max)):\n",
    "    temp_list = nums_min_max[i]  # 获得对应簇数值特征数据\n",
    "    temp = np.concatenate((temp_list, [temp_list[0]]))  # 完成闭合\n",
    "    ax.plot(angles, temp, 'o-', color=colors[i], label=i)\n",
    "\n",
    "# 4、添加标签说明，显示雷达图\n",
    "ax.set_thetagrids(angles * 180 / np.pi, labels)  # 设置极坐标\n",
    "ax.set_rlim(-0.2, 1.2)  # 设置半径刻度\n",
    "\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('数值特征对比分析')\n",
    "plt.show()"
   ]
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    " 结论：\n",
    " - <font color=blue> 聚类1（标签0）：转化高。</font> 在中等的日均UV表现下，有较高的平均注册率、访问深度以及订单转化率，可见该渠道整体转化率较高。 \n",
    "<font color=blue> 高转化，高质量，重点提高订单转化率。</font>\n",
    " - <font color=#DAA520> 聚类2（标签1）：欠佳。</font> 除了访问深度较高外，其他特征都属于极低的层次，因此广告媒体效果质量欠佳，且占比达39%，属主体渠道。<font color=#DAA520> 低性价比，重点考虑投放价值。</font>\n",
    " - <font color=red> 聚类3（标签2）：综合效果好。</font> 除了平均注册率较差，在平均搜索量，日均UV、订单转化率等广告效果指标都表现得不错，是一类综合效果比较好的媒体类。占比35%，比较理想。<font color=red> 适用于各种场景下的广告投放。</font>\n",
    " - <font color=#32CD32> 聚类4（标签3）：引流。</font> 日均UV和平均搜索量的表现比较突出，尤其是日均UV，但其他特征效果不明显，符合引流角色定位。<font color=#32CD32> 符合广告本身诉求，适合拉新场景使用。</font>"
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